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Volume 39, Issue 2 (2024)
GeoRes 2024, 39(2): 243-252 |
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Received: 2024/05/17 | Accepted: 2024/07/3 | Published: 2024/07/27
Received: 2024/05/17 | Accepted: 2024/07/3 | Published: 2024/07/27
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Ahmadi J, Maddahi S, Mirzaei R. Spatial Configuration of Contemporary Residential Buildings in Mashhad City, Iran. GeoRes 2024; 39 (2) :243-252
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1- Department of Art and Architecture, Birjand Branch, Islamic Azad University, Birjand, Iran
2- Department of Art and Architecture, Faculty of Architecture and Urban Planning, University of Science and Technology, Tehran, Iran
2- Department of Art and Architecture, Faculty of Architecture and Urban Planning, University of Science and Technology, Tehran, Iran
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Background
Spatial configuration refers to the arrangement of spaces and the relationships between them, which affect human activities, social interactions, comfort, building energy performance, and architectural quality. This concept is analyzed using methods such as space syntax and is particularly useful for understanding the spatial structure and function of architectural spaces, especially in housing.
Previous Studies
Previous studies have examined the impact of spatial configuration on social behaviors, spatial cognition, and environmental performance. Jafari et al. (2019) have highlighted the role of configuration in shaping social behavior. Jiang (1998) emphasizes the reciprocal relationship between spatial configuration and spatial cognition, describing it as a driving force behind human activities. Dideban et al. (2013) defines spatial cognition as the understanding of environmental positions and features. Bafna (2003), using graph-based methods, consideres configuration analysis a means of eliminating unnecessary complexities in design. Similarly, researchers such as Legeby (2013) and Zerouati and Bellal (2020) have introduced graph analysis as a tool for examining the overall spatial system. Furthermore, studies by Rodrigues et al. (2014), Du et al. (2020), and Musau and Steemers (2008) have investigated the influence of spatial configuration on building energy performance, while Goldstein et al. (2011) demonstrate its role in environmental comfort.
Aim(s)
This study aims to identify general patterns of spatial configuration in contemporary residential building plans in the city of Mashhad by analyzing the spatial layout through graph networks and examining the percentage of area allocated to each space.
Research Type
This study was conducted using a mixed qualitative-quantitative approach.
Research Society, Place and Time
The statistical population of this study included all residential buildings in the city of Mashhad. The study was conducted in 2021, and the research location was Mashhad, Iran.
Sampling Method and Number
The sampling method in this study was a combination of random and cluster sampling. First, one of the 17 districts of Mashhad was randomly selected. Then, a building was randomly chosen from that district, and finally, one of its floors was randomly selected, and its floor plan was recorded. This process continued until theoretical saturation was reached, i.e., the point at which no new data were added to the analysis. In total, 61 residential floor plans with diverse spatial configurations were selected and analyzed.
Used Devices & Materials
In this study, the primary tool for data analysis was SPSS software, version 27, which was used to perform statistical analyses such as Pearson correlation tests. For spatial analysis of the floor plans, the Space Syntax method was employed, including analyses such as spatial adjacency graphs, spatial depth, connectivity, control, and choice. The data used consisted of architectural plans of residential units in the city of Mashhad, which were manually surveyed, coded, and digitized. These plans were then prepared for analysis in three categories: adjacency graphs, dimensions of interior spaces, and spatial hierarchy.
Findings by Text
To develop designs rooted in the spatial characteristics of Mashhad, the spatial configuration of residential housing in the city was examined comprehensively across three dimensions: topology, spatial dimensions, and geometry. The analysis consisted of three components: adjacency graphs (Space Syntax parameters such as depth, connectivity, control, and choice – qualitatively analyzed), dimensional analysis of interior spaces (using coded descriptive statistics), and a quantitative-qualitative spatial hierarchy analysis (via a two-dimensional diagram).
Based on 61 residential floor plans, 44 adjacency graphs were drawn, where the boundary between private and public space was typically defined by the entrance or the living room (Figure 3). Spatial depth ranged from 1 to 5; an increase in intermediary spaces correlated with greater spatial hierarchy and depth, and a decrease in maximum control. The highest control value was 7 and the lowest 3. Connectivity ranged between 2 and 4, and choice values between 1 and 2.
Most graphs corresponded to one or two plans, but the most frequent configuration involved a six-node graph (entrance, living room, master bedroom, child’s room, kitchen, bathroom), followed by a seven-node version substituting a dedicated bathroom .
Using a mixed-method approach, a hierarchy ranking (1–10) of spaces from public to private was established. The entrance and living room were the most public, while the bedroom bathroom was the most private (Figure 1). Three overall categories emerged: public (entrance, living room), semi-private (kitchen, bathroom), and private (bedrooms, dedicated bath) (Figure 2). The greatest variation in rank occurred in the bathroom, master bedroom, child’s bedroom, living room, and toilet.
Figure 1. The hierarchical position of private–public spaces in the main areas across all floor plans
Figure 2) Average hierarchical position of private–public spaces in the main areas across floor plans
Descriptive statistics of spatial areas showed the living room occupied the largest share (median 41%), followed by the combined bedrooms (median 29%). Other spaces like the kitchen, bathroom, and toilet had smaller shares (Table 1), and the percentage range relative to total unit size was also reported.
Table 1. Descriptive statistics of field survey results
Pearson correlation analysis between the size of individual spaces and total unit area indicated the strongest correlations were between bedroom area and bath/toilet (0.648), and bedrooms and living room (0.630). The living room area had the highest correlation with total unit size (0.915) (Table 2).
Table 2. Correlation test of interior space areas in residential units
Further correlation tests on percentage area revealed mostly inverse relationships, for example, a larger bedroom area was associated with a relative reduction in the area of the living room and bathroom (Table 3).
Table 3. Correlation test of the percentage of interior space areas in residential units
Main Comparisons to Similar Studies
The main comparisons of this study with previous research show that the use of graph networks as a primary tool for analyzing spatial configuration has also been emphasized in earlier studies [Hamedani Golshan, 2015; Hosseini et al., 2016]. However, this study advances prior work by applying the method on a larger scale and incorporating the area of each space. The critical role of spatial hierarchy and the significance of spatial location and interrelationships in enhancing plan functionality have similarly been underlined in previous research [Ansari, 2015; Rahmati Gavari et al., 2019]. Earlier studies have mostly focused on traditional architectural plans, indicating that privacy has diminished in modern buildings due to the removal of intermediary spaces that were commonly present in traditional layouts [Alitajer & Nojoumi, 2016; Ergün et al., 2022]. Furthermore, while central spaces in traditional houses were typically courtyards, this function has shifted to the living and reception areas in contemporary housing [Chegini et al., 2020; Latifi & Mahdavinejad, 2022; Fath Baghali et al., 2021]. Overall, the findings of this study align with prior works, reinforcing the notion of significant changes in spatial hierarchy and privacy in modern housing, an issue that has rarely been addressed with such an extensive dataset.
Suggestions
It is recommended that future studies conduct a comparative analysis of traditional and contemporary housing in Mashhad within a single research framework, focusing on a limited number of spatial patterns. Additionally, the impact of factors examined in this study, such as spatial relationships and the area of each space, on the functional performance of the floor plan and user satisfaction should be investigated. Furthermore, while this research was conducted at the city level in Mashhad, similar studies could be carried out in other cities to enable comparative analysis. It is also suggested that future research be conducted with a controlled range of plan sizes.
Conclusion
The main structure of all the floor plans is defined by an entrance space leading to the living room. In most cases, particularly when there is no separate sitting area, the kitchen is directly connected to the living room. In the majority of the plans, spatial hierarchy divisions are well maintained. In some layouts, an intermediate space serves as the central area, while in others, this space has been omitted and the living room assumes the central role.
Acknowledgments: The authors express my their gratitude to everyone participated in this study.
Ethical Permission: None reported by the authors.
Conflict of Interest: None reported by the authors.
Authors’ Contributions: Ahmadi J (First author), Main Researcher/Introduction Writer/Discussion Writer/Methodologist/Statistical Analyst (80%); Maddahi SM (Second author), Discussion Writer (10%); Mirzaei R (Third author), Introduction Writer (10%).
Funding: The research costs were fully covered by the first author.
Spatial configuration refers to the arrangement of spaces and the relationships between them, which affect human activities, social interactions, comfort, building energy performance, and architectural quality. This concept is analyzed using methods such as space syntax and is particularly useful for understanding the spatial structure and function of architectural spaces, especially in housing.
Previous Studies
Previous studies have examined the impact of spatial configuration on social behaviors, spatial cognition, and environmental performance. Jafari et al. (2019) have highlighted the role of configuration in shaping social behavior. Jiang (1998) emphasizes the reciprocal relationship between spatial configuration and spatial cognition, describing it as a driving force behind human activities. Dideban et al. (2013) defines spatial cognition as the understanding of environmental positions and features. Bafna (2003), using graph-based methods, consideres configuration analysis a means of eliminating unnecessary complexities in design. Similarly, researchers such as Legeby (2013) and Zerouati and Bellal (2020) have introduced graph analysis as a tool for examining the overall spatial system. Furthermore, studies by Rodrigues et al. (2014), Du et al. (2020), and Musau and Steemers (2008) have investigated the influence of spatial configuration on building energy performance, while Goldstein et al. (2011) demonstrate its role in environmental comfort.
Aim(s)
This study aims to identify general patterns of spatial configuration in contemporary residential building plans in the city of Mashhad by analyzing the spatial layout through graph networks and examining the percentage of area allocated to each space.
Research Type
This study was conducted using a mixed qualitative-quantitative approach.
Research Society, Place and Time
The statistical population of this study included all residential buildings in the city of Mashhad. The study was conducted in 2021, and the research location was Mashhad, Iran.
Sampling Method and Number
The sampling method in this study was a combination of random and cluster sampling. First, one of the 17 districts of Mashhad was randomly selected. Then, a building was randomly chosen from that district, and finally, one of its floors was randomly selected, and its floor plan was recorded. This process continued until theoretical saturation was reached, i.e., the point at which no new data were added to the analysis. In total, 61 residential floor plans with diverse spatial configurations were selected and analyzed.
Used Devices & Materials
In this study, the primary tool for data analysis was SPSS software, version 27, which was used to perform statistical analyses such as Pearson correlation tests. For spatial analysis of the floor plans, the Space Syntax method was employed, including analyses such as spatial adjacency graphs, spatial depth, connectivity, control, and choice. The data used consisted of architectural plans of residential units in the city of Mashhad, which were manually surveyed, coded, and digitized. These plans were then prepared for analysis in three categories: adjacency graphs, dimensions of interior spaces, and spatial hierarchy.
Findings by Text
To develop designs rooted in the spatial characteristics of Mashhad, the spatial configuration of residential housing in the city was examined comprehensively across three dimensions: topology, spatial dimensions, and geometry. The analysis consisted of three components: adjacency graphs (Space Syntax parameters such as depth, connectivity, control, and choice – qualitatively analyzed), dimensional analysis of interior spaces (using coded descriptive statistics), and a quantitative-qualitative spatial hierarchy analysis (via a two-dimensional diagram).
Based on 61 residential floor plans, 44 adjacency graphs were drawn, where the boundary between private and public space was typically defined by the entrance or the living room (Figure 3). Spatial depth ranged from 1 to 5; an increase in intermediary spaces correlated with greater spatial hierarchy and depth, and a decrease in maximum control. The highest control value was 7 and the lowest 3. Connectivity ranged between 2 and 4, and choice values between 1 and 2.
Most graphs corresponded to one or two plans, but the most frequent configuration involved a six-node graph (entrance, living room, master bedroom, child’s room, kitchen, bathroom), followed by a seven-node version substituting a dedicated bathroom .
Using a mixed-method approach, a hierarchy ranking (1–10) of spaces from public to private was established. The entrance and living room were the most public, while the bedroom bathroom was the most private (Figure 1). Three overall categories emerged: public (entrance, living room), semi-private (kitchen, bathroom), and private (bedrooms, dedicated bath) (Figure 2). The greatest variation in rank occurred in the bathroom, master bedroom, child’s bedroom, living room, and toilet.
Figure 1. The hierarchical position of private–public spaces in the main areas across all floor plans
Figure 2) Average hierarchical position of private–public spaces in the main areas across floor plans
Descriptive statistics of spatial areas showed the living room occupied the largest share (median 41%), followed by the combined bedrooms (median 29%). Other spaces like the kitchen, bathroom, and toilet had smaller shares (Table 1), and the percentage range relative to total unit size was also reported.
Table 1. Descriptive statistics of field survey results
Pearson correlation analysis between the size of individual spaces and total unit area indicated the strongest correlations were between bedroom area and bath/toilet (0.648), and bedrooms and living room (0.630). The living room area had the highest correlation with total unit size (0.915) (Table 2).
Table 2. Correlation test of interior space areas in residential units
Further correlation tests on percentage area revealed mostly inverse relationships, for example, a larger bedroom area was associated with a relative reduction in the area of the living room and bathroom (Table 3).
Table 3. Correlation test of the percentage of interior space areas in residential units
Main Comparisons to Similar Studies
The main comparisons of this study with previous research show that the use of graph networks as a primary tool for analyzing spatial configuration has also been emphasized in earlier studies [Hamedani Golshan, 2015; Hosseini et al., 2016]. However, this study advances prior work by applying the method on a larger scale and incorporating the area of each space. The critical role of spatial hierarchy and the significance of spatial location and interrelationships in enhancing plan functionality have similarly been underlined in previous research [Ansari, 2015; Rahmati Gavari et al., 2019]. Earlier studies have mostly focused on traditional architectural plans, indicating that privacy has diminished in modern buildings due to the removal of intermediary spaces that were commonly present in traditional layouts [Alitajer & Nojoumi, 2016; Ergün et al., 2022]. Furthermore, while central spaces in traditional houses were typically courtyards, this function has shifted to the living and reception areas in contemporary housing [Chegini et al., 2020; Latifi & Mahdavinejad, 2022; Fath Baghali et al., 2021]. Overall, the findings of this study align with prior works, reinforcing the notion of significant changes in spatial hierarchy and privacy in modern housing, an issue that has rarely been addressed with such an extensive dataset.
Suggestions
It is recommended that future studies conduct a comparative analysis of traditional and contemporary housing in Mashhad within a single research framework, focusing on a limited number of spatial patterns. Additionally, the impact of factors examined in this study, such as spatial relationships and the area of each space, on the functional performance of the floor plan and user satisfaction should be investigated. Furthermore, while this research was conducted at the city level in Mashhad, similar studies could be carried out in other cities to enable comparative analysis. It is also suggested that future research be conducted with a controlled range of plan sizes.
Conclusion
The main structure of all the floor plans is defined by an entrance space leading to the living room. In most cases, particularly when there is no separate sitting area, the kitchen is directly connected to the living room. In the majority of the plans, spatial hierarchy divisions are well maintained. In some layouts, an intermediate space serves as the central area, while in others, this space has been omitted and the living room assumes the central role.
Acknowledgments: The authors express my their gratitude to everyone participated in this study.
Ethical Permission: None reported by the authors.
Conflict of Interest: None reported by the authors.
Authors’ Contributions: Ahmadi J (First author), Main Researcher/Introduction Writer/Discussion Writer/Methodologist/Statistical Analyst (80%); Maddahi SM (Second author), Discussion Writer (10%); Mirzaei R (Third author), Introduction Writer (10%).
Funding: The research costs were fully covered by the first author.
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